Bayesian Inference

Follow this topic Ask about this topic

Momentum

20 papers in the last four weeks, up 300% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 219

All topics

To discretize continually: Mean shift interacting particle systems for Bayesian inference
May 13, 2026Ayoub Belhadji, Daniel Sharp, Youssef M. MarzoukBayesian Inference
Center for Computational Science and Engineering · Massachusetts Institute of Technology
Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
May 13, 2026Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2Dynamical Systems Bayesian Inference
Aalto University · Inria SISTM team · Univ. Bordeaux, INSERM BPH, U1219, Inria SISTM team, VRI, France
Correcting Selection Bias in Sparse User Feedback for Large Language Model Quality Estimation: A Multi-Agent Hierarchical Bayesian Approach
May 12, 2026Andrea Morandi, Mahesh ViswanathanLLM Evaluation Bayesian Inference
Cisco Systems, Inc.
Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty
May 12, 2026Joykirat Singh, Zaid Khan, Archiki Prasad +5Partially Observable RL Bayesian Inference
UNC Chapel Hill · Microsoft Research · The University of Texas at Austin
Variational Inference for Lévy Process-Driven SDEs via Neural Tilting
May 11, 2026Yaman Kindap, Manfred Opper, Benjamin Dupuis +2Stochastic Differential Equations Bayesian Inference
1Imperial College London, UK · 2Technical University of Berlin, Germany · 3INRIA, CNRS, Département d’Informatique de l’Ecole Normale Supérieure / PSL, France
Foundations of Reliable Inference: Reliability-Efficiency Co-Design
May 11, 2026Jiayi HuangEfficient Inference Uncertainty Quantification
The Department of EngineeringarXiv:2605.10351v1 King’s College London
ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models
May 11, 2026Wentao Qiu, Guanran Luo, Zhongquan Jian +3Abductive Reasoning Bayesian Networks
School of Informatics, Xiamen University, Xiamen, China · School of Computer and Data Science, Minjiang University, Fuzhou, China · School of Film, Xiamen University, Xiamen, China +2
Supercharging Bayesian Inference with Reliable AI-Informed Priors
May 11, 2026Jongwoo Choi, Sean O'HaganBayesian Inference
Department of Statistics, University of Connecticut · Department of Statistics, University of Chicago
Empirical Bayes 1-bit matrix completion
May 10, 2026Takeru MatsudaEmpirical Bayes Bayesian Inference
Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo · Statistical Mathematics Unit, RIKEN Center for Brain Science
A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility
May 10, 2026Pranava MadhyasthaComputational Cognitive Modeling Bayesian Inference
Dept. of Computer Science, City, University of London · The Alan Turing Institute
PRIM: Meta-Learned Bayesian Root Cause Analysis
May 9, 2026Christopher Lohse, Anish Dhir, Amadou Ba +3Meta-Learning Neural Processes
University of Dublin, Trinity College, Dublin, Ireland · IBM · Gatsby Computational Neuroscience Unit, University College London +2
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
May 8, 2026Nikita Dhawan, Daniel Shen, Leonardo Cotta +1Causal Effect Estimation Bayesian Inference
University of Toronto Vector Institute · University of Waterloo · Vector Institute
Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning
May 8, 2026Chris Elliott, Daniel MurfetStatistical Physics of Learning Bayesian Inference
Timaeus
Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means
May 8, 2026Valentin Kilian, Stefano Cortinovis, François CaronAnytime-Valid Inference Bayesian Inference
Department of Statistics, University of Oxford
BGM-IV: AI-Powered Bayesian Generative Modeling for Instrumental Variable Regression with High-Dimensional Covariates
May 7, 2026Guyue Luo, Haidong Lu, Andrew J. Loza +1Causal Effect Estimation Latent Variable Models
Department of Biostatistics Yale University
A Differentiable Bayesian Relaxation for Latent Partial-Order Inference
May 7, 2026Dongqing Li, Geoff K. Nicholls, Shiyi Sun +1Bayesian Inference
University of Oxford · University College London
LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs
May 7, 2026Chacha Chen, Matthew Jörke, Adam Goliński +4Belief Revision LLM Auditing
1Apple · 2Stanford University · †Work done while an intern at Apple. +1
One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
May 7, 2026Panos Tsimpos, Edoardo Calvello, Ayoub Belhadji +1Amortized Inference Bayesian Inference
Operations Research Center MASSachusetts Institute of Technology Cambridge, MA 02139 · Department of Computing and Mathematical Sciences California Institute of Technology Pasadena, CA 91125 · Laboratory for Information and Decision Systems Center for Computational Science and Engineering MASSachusetts Institute of Technology Cambridge, MA 02139 +2
TinyBayes: Closed-Form Bayesian Inference via Jacobi Prior for Real-Time Image Classification on Edge Devices
May 7, 2026Shouvik Sardar, Sourish DasPlant Disease Classification Edge Inference
Data Science Group Chennai Mathematical Institute
Hypothesis generation and updating in large language models
May 7, 2026Hua-Dong XiongLogical Reasoning Bayesian Inference
School of Psychological and Brain Sciences Georgia Tech
Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference
May 7, 2026Joon Jang, Eunho Jeong, Kyu Sung Choi +1Synthetic-to-Real Domain Adaptation Simulation-Based Inference
Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea · Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea · Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea +3
Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
May 7, 2026Yu Wang, Arnab GangulyStochastic Differential Equations Bayesian Inference
Department of Mathematics Louisiana State University
BALAR : A Bayesian Agentic Loop for Active Reasoning
May 6, 2026Aymen Echarghaoui, Dongxia Wu, Emily B. FoxClarification Question Generation Bayesian Inference
Department of Statistics, Stanford University · Department of Computer Science, Stanford University
Forecasting Oncology Demand Trends with Boosting-Based Bayesian Conjugate Models
May 6, 2026Ademir Batista dos Santos Neto, Tiago Alessandro Espinola Ferreira, Paulo Renato Alves FirminoBayesian Inference Time Series Forecasting
Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discovery
May 5, 2026Amir Rafe, Subasish DasGraph Structure Learning Bayesian Inference
Texas State University San Marcos, USA
Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification
May 5, 2026Nan Feng, Xun HuanUncertainty Quantification Amortized Inference
Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States
MIRA: A Score for Conditional Distribution Accuracy and Model Comparison
May 3, 2026Sammy Sharief, Justine Zeghal, Gabriel Missael Barco +3Bayesian Inference
Mila – Québec AI Institute, Montreal, Quebec, Canada · Ciela Institute, Montreal, Quebec, Canada · Université de Montréal, Montreal, Quebec, Canada +4
PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery
May 3, 2026Xihang Shan, Da ZhouTime-Series Causal Discovery Bayesian Inference
School of Mathematical Sciences Xiamen University Fujian, China
Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
May 1, 2026Jiarui Xing, Song Wang, Jian WangFlow Matching Uncertainty Quantification
Yale University · Harvard Medical School
A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and Thermodynamics
Apr 30, 2026Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah +1Nash Equilibrium Cooperative Game Theory
1DAVID Lab, University of Paris-Saclay, UVSQ, 78035 Versailles, France. · 2TSAM, ASM, Centre for Development of Advanced Technologies, Algiers, Algeria. · 3LIPN, UMR CNRS 7030, Sorbonne Paris Nord University, Villetaneuse, France.

May 13, 2026, stat.MLJ/K move · Enter open · S save

To discretize continually: Mean shift interacting particle systems for Bayesian inference

Ayoub Belhadji, Daniel Sharp, Youssef M. Marzouk

Center for Computational Science and Engineering · Massachusetts Institute of Technology

Bayesian Inference

Open paper AskPDF arXiv

Integration against a probability distribution given its unnormalized density is a central task in Bayesian inference and other fields. We introduce new methods for approximating such expectations with a small set of weighted samples -- i.e., a quadrature rule -- constructed via an interacting particle system that minimizes maximum mean discrepancy (MMD) to the target distribution. These methods extend the classical mean shift algorithm, as well as recent algorithms for optimal quantization of empirical distributions, to the case of continuous distributions. Crucially, our approach creates dynamics for MMD minimization that are invariant to the unknown normalizing constant; they also admit both gradient-free and gradient-informed implementations. The resulting mean shift interacting particle systems converge quickly, capture anisotropy and multi-modality, avoid mode collapse, and scale to high dimensions. We demonstrate their performance on a wide range of benchmark sampling problems, including multi-modal mixtures, Bayesian hierarchical models, PDE-constrained inverse problems, and beyond.

Momentum

Related topics

Latest papers 219